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description Publicationkeyboard_double_arrow_right Article 2024Embargo end date: 01 Jan 2024 SwitzerlandPublisher:IOP Publishing Xiaojin Zhang; Alina Walch; Martin Rüdisüli; Christian Bauer; Peter Burgherr; Russell McKenna; Guillaume Habert;Abstract The transition to renewable energy sources is pivotal in addressing global climate change challenges, with rooftop solar photovoltaic (PV) systems playing a crucial role. For informed decision-making in energy policy, it is important to have a comprehensive understanding of both the economic and environmental performance of rooftop solar PV. This study provides a high-resolution analysis of existing rooftop solar PV systems in Switzerland by assessing the robustness of the potential estimation to properly derive the amount of electricity generated by individual systems, and subsequently quantify the levelized cost of electricity and life cycle greenhouse gas (GHG) emissions of electricity generation from PV and compare them with those of grid electricity supplies. Our results indicate substantial geographical variations between potential estimations and real-world installations, with notable underestimations of approximately 1.3 Gigawatt-peak, primarily for systems around 10 kWp in size, mainly due to the quality of input data and conservative estimation. The study finds that in many regions and for most of the installed capacity, electricity generated from rooftop PV systems is more economical than the grid electricity supply, mainly driven by factors including high electricity prices, larger installations and abundant solar irradiance. The GHG emissions assessment further emphasizes the importance of methodological choice, with stark contrasts between electricity certificate-based approaches and others that are based on the consumption mix. This study suggests the need for more accurate geographical potential estimations, enhanced support for small-scale rooftop PV systems, and more incentives to maximize the potential of their roof area for PV deployment. As Switzerland progresses towards its renewable energy goals, our research underscores the importance of informed policymaking based on a retrospective analysis of existing installations, essential for maximizing the potential and benefits of rooftop solar PV systems.
Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/2634-4505/ad80c3&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024Embargo end date: 01 Jan 2024 SwitzerlandPublisher:IOP Publishing Xiaojin Zhang; Alina Walch; Martin Rüdisüli; Christian Bauer; Peter Burgherr; Russell McKenna; Guillaume Habert;Abstract The transition to renewable energy sources is pivotal in addressing global climate change challenges, with rooftop solar photovoltaic (PV) systems playing a crucial role. For informed decision-making in energy policy, it is important to have a comprehensive understanding of both the economic and environmental performance of rooftop solar PV. This study provides a high-resolution analysis of existing rooftop solar PV systems in Switzerland by assessing the robustness of the potential estimation to properly derive the amount of electricity generated by individual systems, and subsequently quantify the levelized cost of electricity and life cycle greenhouse gas (GHG) emissions of electricity generation from PV and compare them with those of grid electricity supplies. Our results indicate substantial geographical variations between potential estimations and real-world installations, with notable underestimations of approximately 1.3 Gigawatt-peak, primarily for systems around 10 kWp in size, mainly due to the quality of input data and conservative estimation. The study finds that in many regions and for most of the installed capacity, electricity generated from rooftop PV systems is more economical than the grid electricity supply, mainly driven by factors including high electricity prices, larger installations and abundant solar irradiance. The GHG emissions assessment further emphasizes the importance of methodological choice, with stark contrasts between electricity certificate-based approaches and others that are based on the consumption mix. This study suggests the need for more accurate geographical potential estimations, enhanced support for small-scale rooftop PV systems, and more incentives to maximize the potential of their roof area for PV deployment. As Switzerland progresses towards its renewable energy goals, our research underscores the importance of informed policymaking based on a retrospective analysis of existing installations, essential for maximizing the potential and benefits of rooftop solar PV systems.
Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/2634-4505/ad80c3&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/2634-4505/ad80c3&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Switzerland, SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Martin Rüdisüli;Solar photovoltaics (PV) will play an important role in decarbonising the energy system. To date, most assessments of PV transition pathways focus on least-cost aspects, without neither considering the time needed to achieve a substantial PV deployment, nor the impacts on regional electricity supply equality. In this work, we propose two alternative PV expansion strategies for Switzerland: The first strategy prioritises the most pro-ductive roofs and reaches national PV targets by exploiting the minimum number of rooftops, while the second strategy aims at maximising regional self-sufficiency as proxy of PV supply equality. Both strategies are assessed for several PV expansion scenarios using real hourly PV potential data for the entire Swiss building stock. The scenarios are compared to hourly electricity demand profiles for the residential and service sector. Results suggest that when employing the first strategy, at least 46% of suitable rooftops - mostly large roofs with low tilt angles - are needed to reach Switzerland's 2050 PV expansion target of 35 TWh. For the projected electricity demand in 2050, this leads to annual electricity self-sufficiency in about 40% of Swiss districts. This percentage can be increased to over 70% by following strategy two to maximise self-sufficiency - which may feature sev-eral economic and societal advantages - at the cost of covering 86% of suitable rooftops with PV. The findings may support policy makers and local utilities to find efficient and equitable pathways for a decentralised PV expansion, while at the same time reaching the ambitious national renewable energy targets within due time.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 13 citations 13 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Switzerland, SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Martin Rüdisüli;Solar photovoltaics (PV) will play an important role in decarbonising the energy system. To date, most assessments of PV transition pathways focus on least-cost aspects, without neither considering the time needed to achieve a substantial PV deployment, nor the impacts on regional electricity supply equality. In this work, we propose two alternative PV expansion strategies for Switzerland: The first strategy prioritises the most pro-ductive roofs and reaches national PV targets by exploiting the minimum number of rooftops, while the second strategy aims at maximising regional self-sufficiency as proxy of PV supply equality. Both strategies are assessed for several PV expansion scenarios using real hourly PV potential data for the entire Swiss building stock. The scenarios are compared to hourly electricity demand profiles for the residential and service sector. Results suggest that when employing the first strategy, at least 46% of suitable rooftops - mostly large roofs with low tilt angles - are needed to reach Switzerland's 2050 PV expansion target of 35 TWh. For the projected electricity demand in 2050, this leads to annual electricity self-sufficiency in about 40% of Swiss districts. This percentage can be increased to over 70% by following strategy two to maximise self-sufficiency - which may feature sev-eral economic and societal advantages - at the cost of covering 86% of suitable rooftops with PV. The findings may support policy makers and local utilities to find efficient and equitable pathways for a decentralised PV expansion, while at the same time reaching the ambitious national renewable energy targets within due time.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 13 citations 13 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 Switzerland, United Kingdom, SwitzerlandPublisher:Elsevier BV Authors: Walch, A; Mohajeri, N; Gudmundsson, A; Scartezzini, JL;Abstract The extraction of shallow geothermal energy using borehole heat exchangers (BHEs) is a promising approach for decarbonisation of the heating sector. However, a dense deployment of BHEs may lead to thermal interference between neighboring boreholes and thereby to over-exploitation of the heat capacity of the ground. Here we propose a novel method to estimate the technical potential of BHEs which takes into account potential thermal interference as well as the available area for BHE installations. The method combines simulation of the long-term heat extraction through BHEs for a range of borehole spacings and depths and includes an optimisation step to maximise the heat extraction. Application of the method to a case study in western Switzerland, from an available area of 284 km 2 , yields an annual technical potential of 4.65 TWh and a maximum energy density of 15.5 kWh / m 2 . The results also suggest that, for a minimum borehole spacing of 5 m and a maximum borehole depth of 200 m , the cumulative installed borehole depth should not exceed 2 km / ha . The estimated technical potential can be used by urban planners for the techno-economic analysis of BHE systems and by policy makers to develop strategies that encourage the use of shallow geothermal energy.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 43 citations 43 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 Switzerland, United Kingdom, SwitzerlandPublisher:Elsevier BV Authors: Walch, A; Mohajeri, N; Gudmundsson, A; Scartezzini, JL;Abstract The extraction of shallow geothermal energy using borehole heat exchangers (BHEs) is a promising approach for decarbonisation of the heating sector. However, a dense deployment of BHEs may lead to thermal interference between neighboring boreholes and thereby to over-exploitation of the heat capacity of the ground. Here we propose a novel method to estimate the technical potential of BHEs which takes into account potential thermal interference as well as the available area for BHE installations. The method combines simulation of the long-term heat extraction through BHEs for a range of borehole spacings and depths and includes an optimisation step to maximise the heat extraction. Application of the method to a case study in western Switzerland, from an available area of 284 km 2 , yields an annual technical potential of 4.65 TWh and a maximum energy density of 15.5 kWh / m 2 . The results also suggest that, for a minimum borehole spacing of 5 m and a maximum borehole depth of 200 m , the cumulative installed borehole depth should not exceed 2 km / ha . The estimated technical potential can be used by urban planners for the techno-economic analysis of BHE systems and by policy makers to develop strategies that encourage the use of shallow geothermal energy.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 43 citations 43 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Walch, Alina; Li, Xiang; Chambers, Jonathan;This dataset contains an estimation of the useful and technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 400 x 400 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The useful potential is defined as the potential that could be delivered to building heating and cooling systems via a water-to-water heat pump. The datasets contains future scenarios of heating and cooling demand, space cooling equipment deployment (service sector only) and climate change models and considers the potential use of DHC. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The data package contains information on the available area for GSHP systems, the heating and cooling demand as well as the resulting technical and useful potentials for all simulated scenarios of future cooling demand (200 Monte Carlo runs), for the case of direct heat supply (per pixel of 400 x 400 m2) as well as for district heating and cooling (DHC). In scenarios without DHC (direct heat supply), the results are summarized by pixel of 400 x 400 m2. In scenarios with DHC, the results of potentials within DHCs are summarized by DHC (see *_in_dhc.csv) while potentials outside of DHCs are summarized by pixel (see *_outside_dhc.csv). For details on the methodology applied to obtain the results provided in the data package, please refer to the above-mentioned research articles. A description of all files is provided in Dataset documentation.pdf and metadata is provided in Datapackage.json.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Walch, Alina; Li, Xiang; Chambers, Jonathan;This dataset contains an estimation of the useful and technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 400 x 400 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The useful potential is defined as the potential that could be delivered to building heating and cooling systems via a water-to-water heat pump. The datasets contains future scenarios of heating and cooling demand, space cooling equipment deployment (service sector only) and climate change models and considers the potential use of DHC. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The data package contains information on the available area for GSHP systems, the heating and cooling demand as well as the resulting technical and useful potentials for all simulated scenarios of future cooling demand (200 Monte Carlo runs), for the case of direct heat supply (per pixel of 400 x 400 m2) as well as for district heating and cooling (DHC). In scenarios without DHC (direct heat supply), the results are summarized by pixel of 400 x 400 m2. In scenarios with DHC, the results of potentials within DHCs are summarized by DHC (see *_in_dhc.csv) while potentials outside of DHCs are summarized by pixel (see *_outside_dhc.csv). For details on the methodology applied to obtain the results provided in the data package, please refer to the above-mentioned research articles. A description of all files is provided in Dataset documentation.pdf and metadata is provided in Datapackage.json.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Conference object 2019 SwitzerlandPublisher:Elsevier BV Walch, Alina; Castello, Roberto; Mohajeri, Nahid; Guignard, Fabian; Kanevski, Mikhail; Scartezzini, Jean-Louis;Abstract Solar photovoltaic (PV) is one of the most promising technologies for the transition from fossil fuels to renewable energy production. Accurate spatial and temporal modelling of solar irradiance is a key factor in the evaluation of PV technology potential for harvesting solar energy. We present here a data-driven approach based on an ensemble of Extreme Learning Machines using geographic and topographic features in input to predict the global horizontal irradiance in Switzerland from coarse-resolution satellite measurements. This provides a precise mapping of hourly global solar irradiance for each (250 × 250) m2 pixel of a grid covering the entire country. The uncertainty on predicted values is quantified through a variance-based analysis, able to distinguish between model and data uncertainty. The former amounts to 1%, whereas the latter is close to 15% of the predicted values. The presented methodology is scalable and applicable to any large environmental dataset. Our modelling of solar irradiance at hourly temporal resolution and of its uncertainty will allow for an estimate of hourly PV potential in Switzerland to facilitate a more efficient integration of solar photovoltaics into the built environment.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egypro.2019.01.219&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egypro.2019.01.219&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Conference object 2019 SwitzerlandPublisher:Elsevier BV Walch, Alina; Castello, Roberto; Mohajeri, Nahid; Guignard, Fabian; Kanevski, Mikhail; Scartezzini, Jean-Louis;Abstract Solar photovoltaic (PV) is one of the most promising technologies for the transition from fossil fuels to renewable energy production. Accurate spatial and temporal modelling of solar irradiance is a key factor in the evaluation of PV technology potential for harvesting solar energy. We present here a data-driven approach based on an ensemble of Extreme Learning Machines using geographic and topographic features in input to predict the global horizontal irradiance in Switzerland from coarse-resolution satellite measurements. This provides a precise mapping of hourly global solar irradiance for each (250 × 250) m2 pixel of a grid covering the entire country. The uncertainty on predicted values is quantified through a variance-based analysis, able to distinguish between model and data uncertainty. The former amounts to 1%, whereas the latter is close to 15% of the predicted values. The presented methodology is scalable and applicable to any large environmental dataset. Our modelling of solar irradiance at hourly temporal resolution and of its uncertainty will allow for an estimate of hourly PV potential in Switzerland to facilitate a more efficient integration of solar photovoltaics into the built environment.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egypro.2019.01.219&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Roberto Castello; Nahid Mohajeri; Jean-Louis Scartezzini;Abstract The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24 ± 9 TWh . This could meet more than 40% of Switzerland’s current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess Routeshybrid 122 citations 122 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Roberto Castello; Nahid Mohajeri; Jean-Louis Scartezzini;Abstract The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24 ± 9 TWh . This could meet more than 40% of Switzerland’s current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess Routeshybrid 122 citations 122 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Amato, Federico; Guignard, Fabian; Walch, Alina;When using the provided data, please cite the following article: Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859. Summary: This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017. Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades. The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021). Data description: The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf. Data are provided in the pickle format, see https://docs.python.org/3/library/pickle.html#module-pickle. {"references": ["Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859."]}
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 62visibility views 62 download downloads 97 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Amato, Federico; Guignard, Fabian; Walch, Alina;When using the provided data, please cite the following article: Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859. Summary: This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017. Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades. The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021). Data description: The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf. Data are provided in the pickle format, see https://docs.python.org/3/library/pickle.html#module-pickle. {"references": ["Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859."]}
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5500337&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 62visibility views 62 download downloads 97 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Authors: Walch, Alina;The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in: Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” Applied Energy 262 (March 15, 2020): 114404. In the process of generating this dataset, the following aspects were included: Meteorological conditions in Switzerland (solar radiation, temperature, snow cover) Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model) Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva) The panel and inverter efficiencies, as a function of the solar radiation and temperature Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document rooftop_PV_CH_metadata_V1.pdf. Data description: The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided: Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly. {"references": ["Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. \"Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.\" Applied Energy 262 (March 15, 2020): 114404."]} This research has been financed by the Swiss National Science Foundation (SNSF) under the National Research Program 75 (Big Data) for the HyEnergy project.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Authors: Walch, Alina;The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in: Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” Applied Energy 262 (March 15, 2020): 114404. In the process of generating this dataset, the following aspects were included: Meteorological conditions in Switzerland (solar radiation, temperature, snow cover) Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model) Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva) The panel and inverter efficiencies, as a function of the solar radiation and temperature Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document rooftop_PV_CH_metadata_V1.pdf. Data description: The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided: Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly. {"references": ["Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. \"Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.\" Applied Energy 262 (March 15, 2020): 114404."]} This research has been financed by the Swiss National Science Foundation (SNSF) under the National Research Program 75 (Big Data) for the HyEnergy project.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Walch, Alina;This dataset contains an estimation of the technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 200 x 200 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The estimated potential accounts for: Norms for geothermal installations set by the Swiss Society of Engineers and Architects (SIA 384/6) Thermal interferences between neighbouring boreholes and their impact on the temperature change in the ground Topographic Landscape data to assess the available area for BHE installation The methodology used to generate the data is described in: Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. Renewable Energy 165 (2021): 369–80. https://doi.org/10.1016/j.renene.2020.11.019. Dataset description As the data is targeted to large-scale applications and potential studies, it is shared in the format of pixels of 200 x 200 m2. Upon request it can be provided at different aggregation levels, as it is generated at the resolution of individual building units (parcels). The potential is provided as annual values, and it can be converted to monthly values using the provided heating degree weights. For each pixel of 200 x 200 m2, we provide the following variables: Annual total technical heat extraction potential (in MWh) Potential heat delivered to buildings (heat pump output), assuming a heat pump performance (COP) of 4.5 (in MWh) Available area for GSHP installation (in m2) Number of installed boreholes Average heat extraction rate (in W/m) Average borehole depth (in m) Average borehole spacing within the parcels located in the pixel (in m) Heating degree weights (i.e. heat demand variation) for each month A description of the metadata is provided in the document gshp_VD_GE_metadata_V1.pdf. This work is part of the PhD Thesis of Alina Walch. {"references": ["Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. 'Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale'. Renewable Energy 165 (2021): 369\u201380. https://doi.org/10.1016/j.renene.2020.11.019."]}
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Walch, Alina;This dataset contains an estimation of the technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 200 x 200 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The estimated potential accounts for: Norms for geothermal installations set by the Swiss Society of Engineers and Architects (SIA 384/6) Thermal interferences between neighbouring boreholes and their impact on the temperature change in the ground Topographic Landscape data to assess the available area for BHE installation The methodology used to generate the data is described in: Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. Renewable Energy 165 (2021): 369–80. https://doi.org/10.1016/j.renene.2020.11.019. Dataset description As the data is targeted to large-scale applications and potential studies, it is shared in the format of pixels of 200 x 200 m2. Upon request it can be provided at different aggregation levels, as it is generated at the resolution of individual building units (parcels). The potential is provided as annual values, and it can be converted to monthly values using the provided heating degree weights. For each pixel of 200 x 200 m2, we provide the following variables: Annual total technical heat extraction potential (in MWh) Potential heat delivered to buildings (heat pump output), assuming a heat pump performance (COP) of 4.5 (in MWh) Available area for GSHP installation (in m2) Number of installed boreholes Average heat extraction rate (in W/m) Average borehole depth (in m) Average borehole spacing within the parcels located in the pixel (in m) Heating degree weights (i.e. heat demand variation) for each month A description of the metadata is provided in the document gshp_VD_GE_metadata_V1.pdf. This work is part of the PhD Thesis of Alina Walch. {"references": ["Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. 'Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale'. Renewable Energy 165 (2021): 369\u201380. https://doi.org/10.1016/j.renene.2020.11.019."]}
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 7visibility views 7 download downloads 5 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article 2024Embargo end date: 01 Jan 2024 SwitzerlandPublisher:IOP Publishing Xiaojin Zhang; Alina Walch; Martin Rüdisüli; Christian Bauer; Peter Burgherr; Russell McKenna; Guillaume Habert;Abstract The transition to renewable energy sources is pivotal in addressing global climate change challenges, with rooftop solar photovoltaic (PV) systems playing a crucial role. For informed decision-making in energy policy, it is important to have a comprehensive understanding of both the economic and environmental performance of rooftop solar PV. This study provides a high-resolution analysis of existing rooftop solar PV systems in Switzerland by assessing the robustness of the potential estimation to properly derive the amount of electricity generated by individual systems, and subsequently quantify the levelized cost of electricity and life cycle greenhouse gas (GHG) emissions of electricity generation from PV and compare them with those of grid electricity supplies. Our results indicate substantial geographical variations between potential estimations and real-world installations, with notable underestimations of approximately 1.3 Gigawatt-peak, primarily for systems around 10 kWp in size, mainly due to the quality of input data and conservative estimation. The study finds that in many regions and for most of the installed capacity, electricity generated from rooftop PV systems is more economical than the grid electricity supply, mainly driven by factors including high electricity prices, larger installations and abundant solar irradiance. The GHG emissions assessment further emphasizes the importance of methodological choice, with stark contrasts between electricity certificate-based approaches and others that are based on the consumption mix. This study suggests the need for more accurate geographical potential estimations, enhanced support for small-scale rooftop PV systems, and more incentives to maximize the potential of their roof area for PV deployment. As Switzerland progresses towards its renewable energy goals, our research underscores the importance of informed policymaking based on a retrospective analysis of existing installations, essential for maximizing the potential and benefits of rooftop solar PV systems.
Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/2634-4505/ad80c3&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/2634-4505/ad80c3&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024Embargo end date: 01 Jan 2024 SwitzerlandPublisher:IOP Publishing Xiaojin Zhang; Alina Walch; Martin Rüdisüli; Christian Bauer; Peter Burgherr; Russell McKenna; Guillaume Habert;Abstract The transition to renewable energy sources is pivotal in addressing global climate change challenges, with rooftop solar photovoltaic (PV) systems playing a crucial role. For informed decision-making in energy policy, it is important to have a comprehensive understanding of both the economic and environmental performance of rooftop solar PV. This study provides a high-resolution analysis of existing rooftop solar PV systems in Switzerland by assessing the robustness of the potential estimation to properly derive the amount of electricity generated by individual systems, and subsequently quantify the levelized cost of electricity and life cycle greenhouse gas (GHG) emissions of electricity generation from PV and compare them with those of grid electricity supplies. Our results indicate substantial geographical variations between potential estimations and real-world installations, with notable underestimations of approximately 1.3 Gigawatt-peak, primarily for systems around 10 kWp in size, mainly due to the quality of input data and conservative estimation. The study finds that in many regions and for most of the installed capacity, electricity generated from rooftop PV systems is more economical than the grid electricity supply, mainly driven by factors including high electricity prices, larger installations and abundant solar irradiance. The GHG emissions assessment further emphasizes the importance of methodological choice, with stark contrasts between electricity certificate-based approaches and others that are based on the consumption mix. This study suggests the need for more accurate geographical potential estimations, enhanced support for small-scale rooftop PV systems, and more incentives to maximize the potential of their roof area for PV deployment. As Switzerland progresses towards its renewable energy goals, our research underscores the importance of informed policymaking based on a retrospective analysis of existing installations, essential for maximizing the potential and benefits of rooftop solar PV systems.
Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/2634-4505/ad80c3&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert Environmental Resear... arrow_drop_down Environmental Research: Infrastructure and SustainabilityArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/2634-4505/ad80c3&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Switzerland, SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Martin Rüdisüli;Solar photovoltaics (PV) will play an important role in decarbonising the energy system. To date, most assessments of PV transition pathways focus on least-cost aspects, without neither considering the time needed to achieve a substantial PV deployment, nor the impacts on regional electricity supply equality. In this work, we propose two alternative PV expansion strategies for Switzerland: The first strategy prioritises the most pro-ductive roofs and reaches national PV targets by exploiting the minimum number of rooftops, while the second strategy aims at maximising regional self-sufficiency as proxy of PV supply equality. Both strategies are assessed for several PV expansion scenarios using real hourly PV potential data for the entire Swiss building stock. The scenarios are compared to hourly electricity demand profiles for the residential and service sector. Results suggest that when employing the first strategy, at least 46% of suitable rooftops - mostly large roofs with low tilt angles - are needed to reach Switzerland's 2050 PV expansion target of 35 TWh. For the projected electricity demand in 2050, this leads to annual electricity self-sufficiency in about 40% of Swiss districts. This percentage can be increased to over 70% by following strategy two to maximise self-sufficiency - which may feature sev-eral economic and societal advantages - at the cost of covering 86% of suitable rooftops with PV. The findings may support policy makers and local utilities to find efficient and equitable pathways for a decentralised PV expansion, while at the same time reaching the ambitious national renewable energy targets within due time.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 13 citations 13 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Switzerland, SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Martin Rüdisüli;Solar photovoltaics (PV) will play an important role in decarbonising the energy system. To date, most assessments of PV transition pathways focus on least-cost aspects, without neither considering the time needed to achieve a substantial PV deployment, nor the impacts on regional electricity supply equality. In this work, we propose two alternative PV expansion strategies for Switzerland: The first strategy prioritises the most pro-ductive roofs and reaches national PV targets by exploiting the minimum number of rooftops, while the second strategy aims at maximising regional self-sufficiency as proxy of PV supply equality. Both strategies are assessed for several PV expansion scenarios using real hourly PV potential data for the entire Swiss building stock. The scenarios are compared to hourly electricity demand profiles for the residential and service sector. Results suggest that when employing the first strategy, at least 46% of suitable rooftops - mostly large roofs with low tilt angles - are needed to reach Switzerland's 2050 PV expansion target of 35 TWh. For the projected electricity demand in 2050, this leads to annual electricity self-sufficiency in about 40% of Swiss districts. This percentage can be increased to over 70% by following strategy two to maximise self-sufficiency - which may feature sev-eral economic and societal advantages - at the cost of covering 86% of suitable rooftops with PV. The findings may support policy makers and local utilities to find efficient and equitable pathways for a decentralised PV expansion, while at the same time reaching the ambitious national renewable energy targets within due time.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 13 citations 13 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2023.121262&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 Switzerland, United Kingdom, SwitzerlandPublisher:Elsevier BV Authors: Walch, A; Mohajeri, N; Gudmundsson, A; Scartezzini, JL;Abstract The extraction of shallow geothermal energy using borehole heat exchangers (BHEs) is a promising approach for decarbonisation of the heating sector. However, a dense deployment of BHEs may lead to thermal interference between neighboring boreholes and thereby to over-exploitation of the heat capacity of the ground. Here we propose a novel method to estimate the technical potential of BHEs which takes into account potential thermal interference as well as the available area for BHE installations. The method combines simulation of the long-term heat extraction through BHEs for a range of borehole spacings and depths and includes an optimisation step to maximise the heat extraction. Application of the method to a case study in western Switzerland, from an available area of 284 km 2 , yields an annual technical potential of 4.65 TWh and a maximum energy density of 15.5 kWh / m 2 . The results also suggest that, for a minimum borehole spacing of 5 m and a maximum borehole depth of 200 m , the cumulative installed borehole depth should not exceed 2 km / ha . The estimated technical potential can be used by urban planners for the techno-economic analysis of BHE systems and by policy makers to develop strategies that encourage the use of shallow geothermal energy.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 43 citations 43 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 Switzerland, United Kingdom, SwitzerlandPublisher:Elsevier BV Authors: Walch, A; Mohajeri, N; Gudmundsson, A; Scartezzini, JL;Abstract The extraction of shallow geothermal energy using borehole heat exchangers (BHEs) is a promising approach for decarbonisation of the heating sector. However, a dense deployment of BHEs may lead to thermal interference between neighboring boreholes and thereby to over-exploitation of the heat capacity of the ground. Here we propose a novel method to estimate the technical potential of BHEs which takes into account potential thermal interference as well as the available area for BHE installations. The method combines simulation of the long-term heat extraction through BHEs for a range of borehole spacings and depths and includes an optimisation step to maximise the heat extraction. Application of the method to a case study in western Switzerland, from an available area of 284 km 2 , yields an annual technical potential of 4.65 TWh and a maximum energy density of 15.5 kWh / m 2 . The results also suggest that, for a minimum borehole spacing of 5 m and a maximum borehole depth of 200 m , the cumulative installed borehole depth should not exceed 2 km / ha . The estimated technical potential can be used by urban planners for the techno-economic analysis of BHE systems and by policy makers to develop strategies that encourage the use of shallow geothermal energy.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 43 citations 43 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.renene.2020.11.019&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Walch, Alina; Li, Xiang; Chambers, Jonathan;This dataset contains an estimation of the useful and technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 400 x 400 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The useful potential is defined as the potential that could be delivered to building heating and cooling systems via a water-to-water heat pump. The datasets contains future scenarios of heating and cooling demand, space cooling equipment deployment (service sector only) and climate change models and considers the potential use of DHC. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The data package contains information on the available area for GSHP systems, the heating and cooling demand as well as the resulting technical and useful potentials for all simulated scenarios of future cooling demand (200 Monte Carlo runs), for the case of direct heat supply (per pixel of 400 x 400 m2) as well as for district heating and cooling (DHC). In scenarios without DHC (direct heat supply), the results are summarized by pixel of 400 x 400 m2. In scenarios with DHC, the results of potentials within DHCs are summarized by DHC (see *_in_dhc.csv) while potentials outside of DHCs are summarized by pixel (see *_outside_dhc.csv). For details on the methodology applied to obtain the results provided in the data package, please refer to the above-mentioned research articles. A description of all files is provided in Dataset documentation.pdf and metadata is provided in Datapackage.json.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Walch, Alina; Li, Xiang; Chambers, Jonathan;This dataset contains an estimation of the useful and technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 400 x 400 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The useful potential is defined as the potential that could be delivered to building heating and cooling systems via a water-to-water heat pump. The datasets contains future scenarios of heating and cooling demand, space cooling equipment deployment (service sector only) and climate change models and considers the potential use of DHC. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The data package contains information on the available area for GSHP systems, the heating and cooling demand as well as the resulting technical and useful potentials for all simulated scenarios of future cooling demand (200 Monte Carlo runs), for the case of direct heat supply (per pixel of 400 x 400 m2) as well as for district heating and cooling (DHC). In scenarios without DHC (direct heat supply), the results are summarized by pixel of 400 x 400 m2. In scenarios with DHC, the results of potentials within DHCs are summarized by DHC (see *_in_dhc.csv) while potentials outside of DHCs are summarized by pixel (see *_outside_dhc.csv). For details on the methodology applied to obtain the results provided in the data package, please refer to the above-mentioned research articles. A description of all files is provided in Dataset documentation.pdf and metadata is provided in Datapackage.json.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5575318&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Conference object 2019 SwitzerlandPublisher:Elsevier BV Walch, Alina; Castello, Roberto; Mohajeri, Nahid; Guignard, Fabian; Kanevski, Mikhail; Scartezzini, Jean-Louis;Abstract Solar photovoltaic (PV) is one of the most promising technologies for the transition from fossil fuels to renewable energy production. Accurate spatial and temporal modelling of solar irradiance is a key factor in the evaluation of PV technology potential for harvesting solar energy. We present here a data-driven approach based on an ensemble of Extreme Learning Machines using geographic and topographic features in input to predict the global horizontal irradiance in Switzerland from coarse-resolution satellite measurements. This provides a precise mapping of hourly global solar irradiance for each (250 × 250) m2 pixel of a grid covering the entire country. The uncertainty on predicted values is quantified through a variance-based analysis, able to distinguish between model and data uncertainty. The former amounts to 1%, whereas the latter is close to 15% of the predicted values. The presented methodology is scalable and applicable to any large environmental dataset. Our modelling of solar irradiance at hourly temporal resolution and of its uncertainty will allow for an estimate of hourly PV potential in Switzerland to facilitate a more efficient integration of solar photovoltaics into the built environment.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egypro.2019.01.219&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egypro.2019.01.219&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Conference object 2019 SwitzerlandPublisher:Elsevier BV Walch, Alina; Castello, Roberto; Mohajeri, Nahid; Guignard, Fabian; Kanevski, Mikhail; Scartezzini, Jean-Louis;Abstract Solar photovoltaic (PV) is one of the most promising technologies for the transition from fossil fuels to renewable energy production. Accurate spatial and temporal modelling of solar irradiance is a key factor in the evaluation of PV technology potential for harvesting solar energy. We present here a data-driven approach based on an ensemble of Extreme Learning Machines using geographic and topographic features in input to predict the global horizontal irradiance in Switzerland from coarse-resolution satellite measurements. This provides a precise mapping of hourly global solar irradiance for each (250 × 250) m2 pixel of a grid covering the entire country. The uncertainty on predicted values is quantified through a variance-based analysis, able to distinguish between model and data uncertainty. The former amounts to 1%, whereas the latter is close to 15% of the predicted values. The presented methodology is scalable and applicable to any large environmental dataset. Our modelling of solar irradiance at hourly temporal resolution and of its uncertainty will allow for an estimate of hourly PV potential in Switzerland to facilitate a more efficient integration of solar photovoltaics into the built environment.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egypro.2019.01.219&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Roberto Castello; Nahid Mohajeri; Jean-Louis Scartezzini;Abstract The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24 ± 9 TWh . This could meet more than 40% of Switzerland’s current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.
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For further information contact us at helpdesk@openaire.euAccess Routeshybrid 122 citations 122 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 SwitzerlandPublisher:Elsevier BV Authors: Alina Walch; Roberto Castello; Nahid Mohajeri; Jean-Louis Scartezzini;Abstract The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24 ± 9 TWh . This could meet more than 40% of Switzerland’s current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.
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For further information contact us at helpdesk@openaire.euAccess Routeshybrid 122 citations 122 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Amato, Federico; Guignard, Fabian; Walch, Alina;When using the provided data, please cite the following article: Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859. Summary: This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017. Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades. The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021). Data description: The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf. Data are provided in the pickle format, see https://docs.python.org/3/library/pickle.html#module-pickle. {"references": ["Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859."]}
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 62visibility views 62 download downloads 97 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Amato, Federico; Guignard, Fabian; Walch, Alina;When using the provided data, please cite the following article: Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859. Summary: This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017. Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades. The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021). Data description: The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf. Data are provided in the pickle format, see https://docs.python.org/3/library/pickle.html#module-pickle. {"references": ["Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859."]}
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 62visibility views 62 download downloads 97 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Authors: Walch, Alina;The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in: Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” Applied Energy 262 (March 15, 2020): 114404. In the process of generating this dataset, the following aspects were included: Meteorological conditions in Switzerland (solar radiation, temperature, snow cover) Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model) Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva) The panel and inverter efficiencies, as a function of the solar radiation and temperature Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document rooftop_PV_CH_metadata_V1.pdf. Data description: The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided: Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly. {"references": ["Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. \"Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.\" Applied Energy 262 (March 15, 2020): 114404."]} This research has been financed by the Swiss National Science Foundation (SNSF) under the National Research Program 75 (Big Data) for the HyEnergy project.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Authors: Walch, Alina;The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in: Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” Applied Energy 262 (March 15, 2020): 114404. In the process of generating this dataset, the following aspects were included: Meteorological conditions in Switzerland (solar radiation, temperature, snow cover) Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model) Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva) The panel and inverter efficiencies, as a function of the solar radiation and temperature Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document rooftop_PV_CH_metadata_V1.pdf. Data description: The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided: Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly. {"references": ["Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. \"Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.\" Applied Energy 262 (March 15, 2020): 114404."]} This research has been financed by the Swiss National Science Foundation (SNSF) under the National Research Program 75 (Big Data) for the HyEnergy project.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Walch, Alina;This dataset contains an estimation of the technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 200 x 200 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The estimated potential accounts for: Norms for geothermal installations set by the Swiss Society of Engineers and Architects (SIA 384/6) Thermal interferences between neighbouring boreholes and their impact on the temperature change in the ground Topographic Landscape data to assess the available area for BHE installation The methodology used to generate the data is described in: Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. Renewable Energy 165 (2021): 369–80. https://doi.org/10.1016/j.renene.2020.11.019. Dataset description As the data is targeted to large-scale applications and potential studies, it is shared in the format of pixels of 200 x 200 m2. Upon request it can be provided at different aggregation levels, as it is generated at the resolution of individual building units (parcels). The potential is provided as annual values, and it can be converted to monthly values using the provided heating degree weights. For each pixel of 200 x 200 m2, we provide the following variables: Annual total technical heat extraction potential (in MWh) Potential heat delivered to buildings (heat pump output), assuming a heat pump performance (COP) of 4.5 (in MWh) Available area for GSHP installation (in m2) Number of installed boreholes Average heat extraction rate (in W/m) Average borehole depth (in m) Average borehole spacing within the parcels located in the pixel (in m) Heating degree weights (i.e. heat demand variation) for each month A description of the metadata is provided in the document gshp_VD_GE_metadata_V1.pdf. This work is part of the PhD Thesis of Alina Walch. {"references": ["Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. 'Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale'. Renewable Energy 165 (2021): 369\u201380. https://doi.org/10.1016/j.renene.2020.11.019."]}
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Authors: Walch, Alina;This dataset contains an estimation of the technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 200 x 200 m2. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to avoid the over-exploitation of the heat capacity of the ground. We consider GSHPs with vertical closed-loop borehole heat exchangers (BHE) installed at depths of 50 - 200 m. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains. The estimated potential accounts for: Norms for geothermal installations set by the Swiss Society of Engineers and Architects (SIA 384/6) Thermal interferences between neighbouring boreholes and their impact on the temperature change in the ground Topographic Landscape data to assess the available area for BHE installation The methodology used to generate the data is described in: Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. Renewable Energy 165 (2021): 369–80. https://doi.org/10.1016/j.renene.2020.11.019. Dataset description As the data is targeted to large-scale applications and potential studies, it is shared in the format of pixels of 200 x 200 m2. Upon request it can be provided at different aggregation levels, as it is generated at the resolution of individual building units (parcels). The potential is provided as annual values, and it can be converted to monthly values using the provided heating degree weights. For each pixel of 200 x 200 m2, we provide the following variables: Annual total technical heat extraction potential (in MWh) Potential heat delivered to buildings (heat pump output), assuming a heat pump performance (COP) of 4.5 (in MWh) Available area for GSHP installation (in m2) Number of installed boreholes Average heat extraction rate (in W/m) Average borehole depth (in m) Average borehole spacing within the parcels located in the pixel (in m) Heating degree weights (i.e. heat demand variation) for each month A description of the metadata is provided in the document gshp_VD_GE_metadata_V1.pdf. This work is part of the PhD Thesis of Alina Walch. {"references": ["Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. 'Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale'. Renewable Energy 165 (2021): 369\u201380. https://doi.org/10.1016/j.renene.2020.11.019."]}
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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